As a result of increased urbanization, population density, motorization, and general population, traffic congestion has been increasing on transport infrastructures. The increased traffic congestion has impacted the efficiency of travel, air pollution, and fuel consumption. As a result, current transport infrastructure systems are aiming to transition to more intelligent transport systems (ITS) in an effort to manage vehicles, routes, loads, safety measures, fuel consumption, transportation times, and other factors.
One effort of ITS is the implementation and improvement of traffic enforcement cameras, which are used to detect and identify vehicles based on license plate data, such as numbers, characters, states, and other data. For example, some uses of traffic enforcement cameras include toll collection interchanges or booths, speed limit violations, red light violations, bus lane enforcement, level crossing enforcement, high-occupancy vehicle enforcement, turn lane violations, and other uses.
Current automatic license plate recognition (ALPR) systems face challenges due to complications in sensing, weather conditions, and other factors. As a result, a typical state-of-the-art (SOTA) ALPR system imposes specifications on cameras or image quality, camera installation geometry, allowable vehicle speed range, states within scope, and other specifications, to guarantee an acceptable level of performance. Therefore, it may be desirable to have systems and methods for improving the performance of ALPR systems. In particular, it may be desirable to have systems and methods for implementing image preprocessing and closed-loop feedback to improve detection techniques.